AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Chopthin-Consensus Power Sampling (CCPS) is introduced, demonstrating that diversity-preserving resampling and diversity-aware selection are complementary mechanisms for training-free LLM reasoning.
Fairness audits for LLM-based recommenders have largely focused on observable outputs, implicitly assuming that stable recommendations reflect stable internal processing. We challenge this assumption with FairGap, the first benchmark to jointly evaluate recommendation fairness at two levels: observable output shift (OB...
HYMELL is introduced, a hybrid three-level framework for estimating LLM inference latency and energy by combining analytical modeling with machine learning (ML), which enables fast, hardware-free design space exploration and energy-efficient optimization.
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